What makes Avivi unique is its public-private partnership—a reliable approach to solving the last-mile problem can only work if major transit arteries are not clogged by low-capacity vehicles.
Avivi integrates public transit information into the service to allow for reliable route planning and minimal wait times at transfer points.
As a commuter service, Avivi adapts to commuter patterns at different times of day. In the mornings, commuters must arrive regularly at a set time. Commuter pass holders can reserve a daily ride on Avivi shuttles to ensure they arrive at work on time.
At the end of the day, however, patterns tend to shift due to hobbies, overtime, and errands. To answer this call, Avivi uses a different pattern in the evenings, where commuters call vehicles on demand. Avivi shuttles are deployed when a critical mass of riders is reached, but with a comfortable maximum wait time.
The primary point of communication with the service is the app. Here, users can select their plans, plan and track routes, and receive notifications for most convenient connections.
The Avivi key acts as a secondary check-in device to keep commuters safe. It is also used in-shuttle to set personalization settings.
We came together as a team for this project with mutual interest in creating something for social good. During our early meetings, we discussed what current disruptive technologies have potential to have the most positive effects on communities.
Autonomous vehicle technology evokes a range of excitement and fear among the public. We set out to design a use for autonomous vehicles that makes lives better—not only for those who could feasibly own the technology, but for communities at large.
To get ideas flowing, we built a territory map to discover shared concerns among stakeholders in the commuting space. We quickly realized that visualizing these shared concerns would be difficult, since many concerns were shared over different assemblages of stakeholders.
Our final territory map reflected the fact that these lines of concern were blurred, by showing a group of users in the center, surrounded by their granular concerns, with overarching themes in the last layer.
We followed this up with two surveys sent to over 70 participants, asking them about their various attitudes and experiences with commuting and autonomous vehicles.
In the free text entry portions of our survey, we learned that users who default to public transit like it because, at its best, it’s a time to spend enjoying solitude uninterrupted by the stresses of driving. At worst, commuting by public transit is frustrating due to its unpredictability, vulnerability to delays, and pains of connecting from one’s home.
This told us right off the bat that seamlessness is the most attractive selling point for public transit, and if we can amplify that through autonomous vehicles, coupling it with its inherent strengths, we’d have a decent service. As one survey respondent put it:
“I’m happiest when I don’t have to think about my commute and it just goes well.”
We researched five different ride sharing & transit services in detail in order to determine our service model. For each of the companies, we focused on identifying the unique service value, sign-up process, payment models and service structure. In addition to these private companies, we also did research on pilot AV services, like the Mcity Driverless Shuttle at the University of Michigan and May Mobility AV Shuttles in Detroit.
This research was ongoing throughout the design process, but what it ultimately helped us do was determine our own value proposition. Much like Chariot, we wanted to have different types of payment options (All Access vs Credits) to make our service flexible, yet efficient.
We also found interest in how Via licenses their technology to local transportation authorities. In combination with our scenarios and exploratory research, competitive analysis helped us lay a foundation for how we wanted to serve our user.
What was truly pivotal in our development were the interviews we were fortunate enough to conduct with professionals at the forefront of autonomous vehicle research. While many couldn’t discuss their research explicitly, they helped us to begin asking the questions that are driving much of the research happening today.
This wealth of information from stakeholders and experts, along with secondary research, helped us to formulate the design imperatives that guided us through the development of our service.
Our service must prioritize safety.
Our service should consider reliability and comfort.
Our service could address the bigger picture.
This wealth of information from stakeholders and experts, along with secondary research, helped us to formulate the imperatives that guided us through the development of our service.
We developed four unique personas and scenarios that helped us think about what an autonomous ride-sharing service might look like for people of different income levels, different living situations and commuting needs.
We experimented with voice, visual and tactile features – and how they would fit into a system that spanned home, vehicle and work.
Going into scenario-building, we had already scoped in on serving commuters, but had not decided on the type of service we would provide (i.e. shuttle vs on-demand ridesharing vs. private end-to-end service). This exercise was in many ways challenging because it was our first attempt at trying to make sense of unique user experiences and feasible service models at the same time.
Going into testing, we decided to try a branching storyboard model to gut check different components.
We tested with prospective users (namely, commuters who own a car, but do not use it to commute to work).
This helped us to discover not only which touchpoints would be most useful to our target audience, but recurring themes that would help us define what they would want out of our service overall.
What we learned is that users wanted to be able to go on autopilot (a best case scenario when riding public transit currently) and be able to enjoy solitude (the greatest affordance of commuting by car), and just about everything else was superfluous. Anything we created would have to facilitate those things in order to resonate with our target users. Relating back to our imperatives, this translated to a slight reprioritization:
Comfort: Comfort is ease. On short commutes, people value “me time” over added features.
Reliability: To users, reliability means being able to get to work everyday at a certain time, but having flexibility on the way home.
Safety: In the context of a service, people are less concerned about safety then they are about ease.
This all led us to build a multiple-touchpoint system, centered around the service itself.